package com.example.demo.rag;

import com.alibaba.cloud.ai.transformer.splitter.SentenceSplitter;
import jakarta.annotation.Resource;
import org.springframework.ai.document.Document;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.transformer.splitter.TextSplitter;
import org.springframework.ai.vectorstore.SimpleVectorStore;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.pgvector.PgVectorStore;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.jdbc.core.JdbcTemplate;

import java.util.ArrayList;
import java.util.List;

import static org.springframework.ai.vectorstore.pgvector.PgVectorStore.PgDistanceType.COSINE_DISTANCE;
import static org.springframework.ai.vectorstore.pgvector.PgVectorStore.PgIndexType.HNSW;

@Configuration
public class LoveAppVectorStoreConfig {

    @Resource
    private LoveAppDocumentLoader loveAppDocumentLoader;



    private final TextSplitter textSplitter = new SentenceSplitter(150);

    @Bean
    VectorStore loveAppVectorStore(JdbcTemplate pgJdbcTemplate,EmbeddingModel dashscopeEmbeddingModel) {
        VectorStore vectorStore = PgVectorStore.builder(pgJdbcTemplate, dashscopeEmbeddingModel)
                .dimensions(1536)                    // Optional: defaults to model dimensions or 1536
                .distanceType(COSINE_DISTANCE)       // Optional: defaults to COSINE_DISTANCE
                .indexType(HNSW)                     // Optional: defaults to HNSW
                .initializeSchema(true)              // Optional: defaults to false
                .schemaName("public")                // Optional: defaults to "public"
                .vectorTableName("vector_store")     // Optional: defaults to "vector_store"
                .maxDocumentBatchSize(10000)         // Optional: defaults to 10000
                .build();
//        // 加载文档
//        List<Document> documents = loveAppDocumentLoader.loadMarkdowns();
//        for (Document document : documents) {
//            List<Document> documentsNew =  textSplitter.split(document);
//            vectorStore.add(documentsNew);
//        }
        return vectorStore;
    }

}

